def main(training_file_name, year, month, start_date, end_date):
day_list = scraper.days(KOSPI_TICKER, start_date, end_date)
stock_price_list = scraper.stock_prices(day_list, KOSPI_TICKER, start_date, end_date)
population = Population(POPULATION_SIZE, day_list, stock_price_list)
population.get_chromosomes().sort(key=lambda chromosome: chromosome.get_fitness(), reverse=True)
_print_population(population, 0, training_file_name, year, month)
generation_number = 1
while generation_number <= GENERATION_SIZE:
population = GeneticAlgorithm.evolve(population, day_list, stock_price_list, POPULATION_SIZE)
population.get_chromosomes().sort(key=lambda chromosome: chromosome.get_fitness(), reverse=True)
_print_population(population, generation_number, training_file_name, year, month)
generation_number += 1
best_chromosome = population.get_chromosomes()[0]
print("GA OPTIMIZATION ON KOSPI200 FINANCIAL DATA ON", month, ",", year, '-', training_file_name)
print("BEST PF RESULT AFTER GA", best_chromosome)
print("Number of stocks selected in the portfolio: ", best_chromosome.get_num_of_selected_stocks())
print("ROI: ", best_chromosome.get_roi(), " | RISK: ", best_chromosome.get_risk())
print("Fittest chromosome fitness:", best_chromosome.get_fitness())
if generation_number == GENERATION_SIZE:
monthly_best = []
monthly_best_file_name = "Monthly Best-" + str(month) + "_" + str(year) + ".csv"
monthly_best.append([year, month, best_chromosome, best_chromosome.get_fitness(), best_chromosome.get_roi()])
if os.path.exists('../Result/' + str(year) + '/' + monthly_best_file_name):
append_write = 'a' # append if already exists
else:
append_write = 'w' # make a new file if not
writer2 = csv.writer(open('../Result/' + str(year) + '/' + monthly_best_file_name, append_write, newline=''))
writer2.writerows([monthly_best])
def main(file_name):
days = scraper.days(KOSPI_TICKER, STARTDATE, ENDDATE)
stock_prices = scraper.stock_prices(days, KOSPI_TICKER, STARTDATE, ENDDATE)
print("Find " + file_name + " for the further details.")
population = Population(POPULATION_SIZE, days, stock_prices)
population.get_chromosomes().sort(
key=lambda chromosome: chromosome.get_fitness(), reverse=True)
_print_population(population, 1, file_name)
generation_number = 2
while generation_number <= GENERATION_SIZE + 1:
gen_start_time = time.time()
population = GeneticAlgorithm.evolve(population, days, stock_prices)
population.get_chromosomes().sort(
key=lambda chromosome: chromosome.get_fitness(), reverse=True)
_print_population(population, generation_number, file_name)
generation_number += 1
gen_end_time = time.time()
print("generation created in ", gen_end_time - gen_start_time,
"Seconds.")
for count in range(650):
print("=", end="")
print()
print("BEST PF RESULT AFTER GA", population.get_chromosomes()[0])
print("Num of stocks: ",
population.get_chromosomes()[0].get_num_of_selected_stocks(),
" | ROI: ",
population.get_chromosomes()[0].get_roi(), " | RISK: ",
population.get_chromosomes()[0].get_risk())
def main(file_name, month, start_date, end_date):
day_list = scraper.days(KOSPI_TICKER, start_date, end_date)
stock_price_list = scraper.stock_prices(day_list, KOSPI_TICKER, start_date,
end_date)
# print(month_count, ": Find " + file_name + " for the further details.")
population = Population(POPULATION_SIZE, day_list, stock_price_list)
population.get_chromosomes().sort(
key=lambda chromosome: chromosome.get_fitness(), reverse=True)
_print_population(population, 0, file_name, month)
generation_number = 1
while generation_number <= GENERATION_SIZE + 1:
population = GeneticAlgorithm.evolve(population, day_list,
stock_price_list, POPULATION_SIZE)
population.get_chromosomes().sort(
key=lambda chromosome: chromosome.get_fitness(), reverse=True)
_print_population(population, generation_number, file_name, month)
generation_number += 1
print("GA OPTIMIZATION ON KOSPI200 FINANCIAL DATA ON", month, ",", YEAR)
print("BEST PF RESULT AFTER GA", population.get_chromosomes()[0])
print("Number of stocks selected in the portfolio: ",
population.get_chromosomes()[0].get_num_of_selected_stocks(),
" | ROI: ",
population.get_chromosomes()[0].get_roi(), " | RISK: ",
population.get_chromosomes()[0].get_risk())
print("Fittest chromosome fitness:",
population.get_chromosomes()[0].get_fitness())
def main(year, month, ga_start_date, ga_end_date, test_start_date,
test_end_date, index):
initiated_time = time.time()
training_file_name = "Kospi_200_portfolio_optimization-" + str(
datetime.datetime.now().strftime("%Y%m%d_%H_%M_%S")) + ".csv"
day_list = scraper.days(KOSPI_TICKER, ga_start_date, ga_end_date)
stock_price_list = scraper.stock_prices(day_list, KOSPI_TICKER,
ga_start_date, ga_end_date)
test_day_list = scraper.days(KOSPI_TICKER, test_start_date, test_end_date)
test_stock_price_list = scraper.stock_prices(test_day_list, KOSPI_TICKER,
test_start_date,
test_end_date)
population = Population(POPULATION_SIZE, day_list, stock_price_list)
population.get_chromosomes().sort(
key=lambda chromosome: chromosome.get_fitness(), reverse=True)
save.save_population(population, 0, year, month, training_file_name)
generation_number = 1
while generation_number < GENERATION_SIZE:
population = GeneticAlgorithm.evolve(population, day_list,
stock_price_list, POPULATION_SIZE)
population.get_chromosomes().sort(
key=lambda chromosome: chromosome.get_fitness(), reverse=True)
save.save_population(population, generation_number, year, month,
training_file_name)
generation_number += 1
if generation_number == GENERATION_SIZE:
completed_time = time.time()
print(index + 1, "th GA GENERATION OF PORTFOLIO OPTIMIZATION FOR ",
test_start_date, ",", test_end_date, "COMPLETED IN",
completed_time - initiated_time, "SECONDS.")
best_chromosome = population.get_chromosomes()[0]
test_fund.test_best_population(best_chromosome, test_day_list,
test_stock_price_list, year, month,
ga_start_date, ga_end_date,
test_start_date, test_end_date)
def main(training_file_name, year, month, ga_start_date, ga_end_date, test_start_date, test_end_date):
day_list = scraper.days(KOSPI_TICKER, ga_start_date, ga_end_date)
stock_price_list = scraper.stock_prices(day_list, KOSPI_TICKER, ga_start_date, ga_end_date)
test_day_list = scraper.days(KOSPI_TICKER, test_start_date, test_end_date)
test_stock_price_list = scraper.stock_prices(test_day_list, KOSPI_TICKER, test_start_date, test_end_date)
population = Population(POPULATION_SIZE, day_list, stock_price_list)
population.get_chromosomes().sort(key=lambda chromosome: chromosome.get_fitness(), reverse=True)
save.save_population(population, 0, training_file_name, year, month)
generation_number = 1
while generation_number < GENERATION_SIZE:
population = GeneticAlgorithm.evolve(population, day_list, stock_price_list, POPULATION_SIZE)
population.get_chromosomes().sort(key=lambda chromosome: chromosome.get_fitness(), reverse=True)
save.save_population(population, generation_number, training_file_name, year, month)
generation_number += 1
best_chromosome = population.get_chromosomes()[0]
save.print_best_population(best_chromosome, year, month, training_file_name)
if generation_number == GENERATION_SIZE:
monthly_best_file_name = "Kospi_200_portfolio_optimization-Monthly Best.csv"
save.save_best_generation(best_chromosome, year, month, monthly_best_file_name)
test_fund.test_best_population(KOSPI_TICKER, best_chromosome, test_day_list, test_stock_price_list, year, month, monthly_best_file_name)
def main(file_name):
days = scraper.days(KOSPI_TICKER, STARTDATE, ENDDATE)
stock_prices = scraper.stock_prices(days, KOSPI_TICKER, STARTDATE, ENDDATE)
print("Find " + file_name + " for the further details.")
population = Population(POPULATION_SIZE, days, stock_prices)
population.get_chromosomes().sort(
key=lambda chromosome: chromosome.get_fitness(), reverse=True)
_print_population(population, 0, file_name)
generation_number = 1
while generation_number < GENERATION_SIZE:
population = GeneticAlgorithm.evolve(population, days, stock_prices)
population.get_chromosomes().sort(
key=lambda chromosome: chromosome.get_fitness(), reverse=True)
_print_population(population, generation_number, file_name)
generation_number += 1
def main(file_name):
day_list = scraper.days(KOSPI_TICKER, START_DATE, END_DATE)
stock_price_list = scraper.stock_prices(day_list, KOSPI_TICKER, START_DATE, END_DATE)
print("Find " + file_name + " for the further details.")
population = Population(POPULATION_SIZE, day_list, stock_price_list)
population.get_chromosomes().sort(key=lambda chromosome: chromosome.get_fitness(), reverse=True)
_print_population(population, 0, file_name)
generation_number = 1
while generation_number <= GENERATION_SIZE+1:
population = GeneticAlgorithm.evolve(population, day_list, stock_price_list, POPULATION_SIZE)
population.get_chromosomes().sort(key=lambda chromosome: chromosome.get_fitness(), reverse=True)
_print_population(population, generation_number, file_name)
generation_number += 1
print("BEST PF RESULT AFTER GA", population.get_chromosomes()[0])
print("Number of stocks selected in the portfolio: ", population.get_chromosomes()[0].get_num_of_selected_stocks(), " | ROI: ", population.get_chromosomes()[0].get_roi(), " | RISK: ", population.get_chromosomes()[0].get_risk())
print("Fittest chromosome fitness:", population.get_chromosomes()[0].get_fitness())
for count in range(650):
print("=", end="")
print()